DeepGalaxy: Deducing the Properties of Galaxy Mergers from Images Using Deep Neural Networks

Cai, Maxwell X.; Bédorf, Jeroen; Saletore, Vikram A.; Codreanu, Valeriu; Podareanu, Damian; Chaibi, Adel; Qian, Penny Xuran

doi:10.1109/dls51937.2020.00012

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…This is interesting because deep neural networks are used to provide temporal constraints on phases of galaxy formation, based on a single snapshot. A similar approach was followed by Cai et al (2020). They trained a combination of Autoencoders and Variational Autoencoders to infer the properties of galaxy mergers.…”

Section: Physical Processes Of Galaxy Formationmentioning

confidence: 99%

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

Huertas-Company

Lanusse²

2023

Publ. Astron. Soc. Aust.

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The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone from a handful of works in 2015 to an average of one paper per week in 2021 in the area of galaxy surveys. Half a decade after the first published work in astronomy mentioning deep learning, and shortly before new big data sets such as Euclid and LSST start becoming available, we believe it is timely to review what has been the real impact of this new technology in the field and its potential to solve key challenges raised by the size and complexity of the new datasets. The purpose of this review is thus two-fold. We first aim at summarising, in a common document, the main applications of deep learning for galaxy surveys that have emerged so far. We then extract the major achievements and lessons learned and highlight key open questions and limitations, which in our opinion, will require particular attention in the coming years. Overall, state-of-the-art deep learning methods are rapidly adopted by the astronomical community, reflecting a democratisation of these methods. This review shows that the majority of works using deep learning up to date are oriented to computer vision tasks (e.g. classification, segmentation). This is also the domain of application where deep learning has brought the most important breakthroughs so far. However, we also report that the applications are becoming more diverse and deep learning is used for estimating galaxy properties, identifying outliers or constraining the cosmological model. Most of these works remain at the exploratory level though which could partially explain the limited impact in terms of citations. Some common challenges will most likely need to be addressed before moving to the next phase of massive deployment of deep learning in the processing of future surveys; for example, uncertainty quantification, interpretability, data labelling and domain shift issues from training with simulations, which constitutes a common practice in astronomy.

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Section: Physical Processes Of Galaxy Formationmentioning

confidence: 99%

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

Huertas-Company

Lanusse²

2023

Publ. Astron. Soc. Aust.

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Deep Learning assessment of galaxy morphology in S-PLUS Data Release 1

Cortesi

Lucatelli

Dias

et al. 2021

Monthly Notices of the Royal Astronomical Society

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The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation, but the classification of galaxies in large sky surveys is becoming a significant challenge. We use data from the Stripe-82 area observed by the Southern Photometric Local Universe Survey (S-PLUS) in twelve optical bands, and present a catalogue of the morphologies of galaxies brighter than r = 17 mag determined both using a novel multi-band morphometric fitting technique and Convolutional Neural Networks (CNNs) for computer vision. Using the CNNs we find that, compared to our baseline results with 3 bands, the performance increases when using 5 broad and 3 narrow bands, but is poorer when using the full 12 band S-PLUS image set. However, the best result is still achieved with just 3 optical bands when using pre-trained network weights from an ImageNet data set. These results demonstrate the importance of using prior knowledge about neural network weights based on training in unrelated, extensive data sets, when available. Our catalogue contains 3274 galaxies in Stripe-82 that are not present in Galaxy Zoo 1 (GZ1), and we also provide our classifications for 4686 galaxies that were considered ambiguous in GZ1. Finally, we present a prospect of a novel way to take advantage of 12 band information for morphological classification using morphometric features, and we release a model that has been pre-trained on several bands that could be adapted for classifications using data from other surveys. The morphological catalogues are publicly available.

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Transfer Learning and Deep Metric Learning for Automated Galaxy Morphology Representation

2022

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Galaxy morphology characterisation is an important area of study, as the type and formation of galaxies offer insights into the origin and evolution of the universe. Owing to the increased availability of images of galaxies, scientists have turned to crowd-sourcing to automate the process of instance labelling. However, research has shown that using crowd-sourced labels for galaxy classification comes with many pitfalls. An alternative approach to galaxy classification is metric learning. Metric learning allows for improved representations for classification, anomaly detection, information retrieval, clustering and dimensionality reduction. Understanding the implications of this approach regarding crowd-sourced labels is of paramount importance if scientists intend to continue using them. This paper compares metric learning and classification models trained or fine-tuned on both the crowd-sourced Galaxy Zoo 2 (GZ2-H) dataset and expertly labelled EFIGI catalogue. The study uses the Revised Shapley-Ames (RSA) catalogue of bright galaxies, also labelled by experts, as an unseen test set. The RSA catalogue allows for an accurate comparison of the performance of the models at predicting the Hubble types of galaxies. The classification accuracy for the crowd-sourced and expert models indicated that the models are comparable on the surface. However, using alternative metrics, the results show that the models trained on the expert dataset outperformed the model trained on the crowd-sourced data in terms of actual vs predicted labels. Further, the results show that fine-tuning a model pre-trained on crowd-sourced data can outperform the state-of-the-art in galaxy characterisation. The models trained to predict the Hubble types of galaxies are better when fine-tuned using the Proxy-NCA and Normalised-Softmax loss functions than with other pairwise losses. The Normalised-Softmax loss yielded the best overall 9-class models with accuracies at 30.88% (GZ2-H) and 30.05% (EFIGI) and MAP values of 0.3483 (GZ2-H) and 0.3889. The Proxy-NCA loss produced the second-best overall 9-class models with accuracies at 30.33% (GZ2-H) and 20.03% (EFIGI) and MAP values of 0.3577 (GZ2-H) and 0.3917 (EFIGI). Finally, the paper highlights the need for caution when utilising crowd-sourced labels; however, it argues that transfer learning from crowd-sourced labelled data to expert-labelled data can still lead to significant improvements.

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DeepGalaxy: Deducing the Properties of Galaxy Mergers from Images Using Deep Neural Networks

Cited by 3 publications

References 19 publications

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

Deep Learning assessment of galaxy morphology in S-PLUS Data Release 1

Transfer Learning and Deep Metric Learning for Automated Galaxy Morphology Representation

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